
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import evaluate
import numpy as np
import torch
from datasets import load_dataset
from PIL import Image
from torchvision.transforms import (
    CenterCrop,
    Compose,
    Normalize,
    RandomHorizontalFlip,
    RandomResizedCrop,
    Resize,
    ToTensor,
)

import transformers
from transformers import (
    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    AutoModelForImageClassification,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
__lowerCAmelCase     :	Any       			=  logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('4.31.0')

require_version('datasets>=1.8.0', 'To fix: pip install -r examples/pytorch/image-classification/requirements.txt')

__lowerCAmelCase     :	Dict       			=  list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
__lowerCAmelCase     :	int       			=  tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)

def a__       (			A_   ):






  '''simple docstring'''





  with open(A_,	"""rb"""   ) as f:
    __magic_name__								=			Image.open(A_   )
    return im.convert("""RGB"""   )




@dataclass
class 			UpperCAmelCase_     :


  '''simple docstring'''







  a__       			=						field(
      default=_A    ,			metadata={
          """help""": """Name of a dataset from the hub (could be your own, possibly private dataset hosted on the hub)."""
      }    ,			)
  a__       			=						field(
      default=_A    ,			metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}	)
  a__       			=						field(default=_A    ,			metadata={"""help""": """A folder containing the training data."""}	)
  a__       			=						field(default=_A    ,			metadata={"""help""": """A folder containing the validation data."""}	)
  a__       			=						field(
      default=0.15    ,			metadata={"""help""": """Percent to split off of train for validation."""}	)
  a__       			=						field(
      default=_A    ,			metadata={
          """help""": (
              """For debugging purposes or quicker training, truncate the number of training examples to this """
              """value if set."""
          )
      }    ,			)
  a__       			=						field(
      default=_A    ,			metadata={
          """help""": (
              """For debugging purposes or quicker training, truncate the number of evaluation examples to this """
              """value if set."""
          )
      }    ,			)
  def 				_lowercase				(					self     :					str       )     ->      Any:







    """simple docstring"""






    if self.dataset_name is None and (self.train_dir is None and self.validation_dir is None):
      raise ValueError(
          """You must specify either a dataset name from the hub or a train and/or validation directory."""       )







@dataclass
class 			UpperCAmelCase_     :


  '''simple docstring'''







  a__       			=						field(
      default="""google/vit-base-patch16-224-in21k"""    ,			metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}    ,			)
  a__       			=						field(
      default=_A    ,			metadata={"""help""": """If training from scratch, pass a model type from the list: """ + """, """.join(_A	)}    ,			)
  a__       			=						field(
      default=_A    ,			metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}	)
  a__       			=						field(
      default=_A    ,			metadata={"""help""": """Where do you want to store the pretrained models downloaded from s3"""}	)
  a__       			=						field(
      default="""main"""    ,			metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""}    ,			)
  a__       			=						field(default=_A    ,			metadata={"""help""": """Name or path of preprocessor config."""}	)
  a__       			=						field(
      default=_A    ,			metadata={
          """help""": (
              """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
              """with private models)."""
          )
      }    ,			)
  a__       			=						field(
      default=_A    ,			metadata={"""help""": """Will enable to load a pretrained model whose head dimensions are different."""}    ,			)







def a__       (			A_   ):






  '''simple docstring'''





  __magic_name__								=			torch.stack([example["""pixel_values"""] for example in examples]   )
  __magic_name__								=			torch.tensor([example["""labels"""] for example in examples]   )
  return {"pixel_values": pixel_values, "labels": labels}

def a__       (			):






  '''simple docstring'''





  __magic_name__								=			HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)   )
  if len(sys.argv   ) == 2 and sys.argv[1].endswith(""".json"""   ):
    # If we pass only one argument to the script and it's the path to a json file,
    # let's parse it to get our arguments.
    __magic_name__	,       __magic_name__	,       __magic_name__								=			parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]   )   )
  else:
    __magic_name__	,       __magic_name__	,       __magic_name__								=			parser.parse_args_into_dataclasses()

  # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
  # information sent is the one passed as arguments along with your Python/PyTorch versions.
  send_example_telemetry("""run_image_classification""",	A_,	A_   )

  # Setup logging
  logging.basicConfig(
      format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""",	datefmt="""%m/%d/%Y %H:%M:%S""",	handlers=[logging.StreamHandler(sys.stdout   )],	)

  if training_args.should_log:
    # The default of training_args.log_level is passive, so we set log level at info here to have that default.
    transformers.utils.logging.set_verbosity_info()

  __magic_name__								=			training_args.get_process_log_level()
  logger.setLevel(A_   )
  transformers.utils.logging.set_verbosity(A_   )
  transformers.utils.logging.enable_default_handler()
  transformers.utils.logging.enable_explicit_format()

  # Log on each process the small summary:
  logger.warning(
      f'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
      + f'''distributed training: {bool(training_args.local_rank != -1   )}, 16-bits training: {training_args.fpaa}'''   )
  logger.info(f'''Training/evaluation parameters {training_args}'''   )

  # Detecting last checkpoint.
  __magic_name__								=			None
  if os.path.isdir(training_args.output_dir   ) and training_args.do_train and not training_args.overwrite_output_dir:
    __magic_name__								=			get_last_checkpoint(training_args.output_dir   )
    if last_checkpoint is None and len(os.listdir(training_args.output_dir   )   ) > 0:
      raise ValueError(
          f'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
          """Use --overwrite_output_dir to overcome."""   )
    elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
      logger.info(
          f'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
          """the `--output_dir` or add `--overwrite_output_dir` to train from scratch."""   )

    # Set seed before initializing model.
  set_seed(training_args.seed   )

  # Initialize our dataset and prepare it for the 'image-classification' task.
  if data_args.dataset_name is not None:
    __magic_name__								=			load_dataset(
        data_args.dataset_name,	data_args.dataset_config_name,	cache_dir=model_args.cache_dir,	task="""image-classification""",	use_auth_token=True if model_args.use_auth_token else None,	)
  else:
    __magic_name__								=			{}
    if data_args.train_dir is not None:
      __magic_name__								=			os.path.join(data_args.train_dir,	"""**"""   )
    if data_args.validation_dir is not None:
      __magic_name__								=			os.path.join(data_args.validation_dir,	"""**"""   )
    __magic_name__								=			load_dataset(
        """imagefolder""",	data_files=A_,	cache_dir=model_args.cache_dir,	task="""image-classification""",	)

  # If we don't have a validation split, split off a percentage of train as validation.
  __magic_name__								=			None if """validation""" in dataset.keys() else data_args.train_val_split
  if isinstance(data_args.train_val_split,	A_   ) and data_args.train_val_split > 0.0:
    __magic_name__								=			dataset["""train"""].train_test_split(data_args.train_val_split   )
    __magic_name__								=			split["""train"""]
    __magic_name__								=			split["""test"""]

  # Prepare label mappings.
  # We'll include these in the model's config to get human readable labels in the Inference API.
  __magic_name__								=			dataset["""train"""].features["""labels"""].names
  __magic_name__	,       __magic_name__								=			{}, {}
  for i, label in enumerate(A_   ):
    __magic_name__								=			str(A_   )
    __magic_name__								=			label

  # Load the accuracy metric from the datasets package
  __magic_name__								=			evaluate.load("""accuracy"""   )

  # Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
  # predictions and label_ids field) and has to return a dictionary string to float.
  def compute_metrics(A_   ):
    return metric.compute(predictions=np.argmax(p.predictions,	axis=1   ),	references=p.label_ids   )

  __magic_name__								=			AutoConfig.from_pretrained(
      model_args.config_name or model_args.model_name_or_path,	num_labels=len(A_   ),	labelaid=A_,	idalabel=A_,	finetuning_task="""image-classification""",	cache_dir=model_args.cache_dir,	revision=model_args.model_revision,	use_auth_token=True if model_args.use_auth_token else None,	)
  __magic_name__								=			AutoModelForImageClassification.from_pretrained(
      model_args.model_name_or_path,	from_tf=bool(""".ckpt""" in model_args.model_name_or_path   ),	config=A_,	cache_dir=model_args.cache_dir,	revision=model_args.model_revision,	use_auth_token=True if model_args.use_auth_token else None,	ignore_mismatched_sizes=model_args.ignore_mismatched_sizes,	)
  __magic_name__								=			AutoImageProcessor.from_pretrained(
      model_args.image_processor_name or model_args.model_name_or_path,	cache_dir=model_args.cache_dir,	revision=model_args.model_revision,	use_auth_token=True if model_args.use_auth_token else None,	)

  # Define torchvision transforms to be applied to each image.
  if "shortest_edge" in image_processor.size:
    __magic_name__								=			image_processor.size["""shortest_edge"""]
  else:
    __magic_name__								=			(image_processor.size["""height"""], image_processor.size["""width"""])
  __magic_name__								=			Normalize(mean=image_processor.image_mean,	std=image_processor.image_std   )
  __magic_name__								=			Compose(
      [
          RandomResizedCrop(A_   ),
          RandomHorizontalFlip(),
          ToTensor(),
          normalize,
      ]   )
  __magic_name__								=			Compose(
      [
          Resize(A_   ),
          CenterCrop(A_   ),
          ToTensor(),
          normalize,
      ]   )

  def train_transforms(A_   ):
    __magic_name__								=			[
        _train_transforms(pil_img.convert("""RGB"""   )   ) for pil_img in example_batch["""image"""]
    ]
    return example_batch

  def val_transforms(A_   ):
    __magic_name__								=			[_val_transforms(pil_img.convert("""RGB"""   )   ) for pil_img in example_batch["""image"""]]
    return example_batch

  if training_args.do_train:
    if "train" not in dataset:
      raise ValueError("""--do_train requires a train dataset"""   )
    if data_args.max_train_samples is not None:
      __magic_name__								=			(
          dataset["""train"""].shuffle(seed=training_args.seed   ).select(range(data_args.max_train_samples   )   )
      )
    # Set the training transforms
    dataset["train"].set_transform(A_   )

  if training_args.do_eval:
    if "validation" not in dataset:
      raise ValueError("""--do_eval requires a validation dataset"""   )
    if data_args.max_eval_samples is not None:
      __magic_name__								=			(
          dataset["""validation"""].shuffle(seed=training_args.seed   ).select(range(data_args.max_eval_samples   )   )
      )
    # Set the validation transforms
    dataset["validation"].set_transform(A_   )

  # Initalize our trainer
  __magic_name__								=			Trainer(
      model=A_,	args=A_,	train_dataset=dataset["""train"""] if training_args.do_train else None,	eval_dataset=dataset["""validation"""] if training_args.do_eval else None,	compute_metrics=A_,	tokenizer=A_,	data_collator=A_,	)

  # Training
  if training_args.do_train:
    __magic_name__								=			None
    if training_args.resume_from_checkpoint is not None:
      __magic_name__								=			training_args.resume_from_checkpoint
    elif last_checkpoint is not None:
      __magic_name__								=			last_checkpoint
    __magic_name__								=			trainer.train(resume_from_checkpoint=A_   )
    trainer.save_model()
    trainer.log_metrics("""train""",	train_result.metrics   )
    trainer.save_metrics("""train""",	train_result.metrics   )
    trainer.save_state()

  # Evaluation
  if training_args.do_eval:
    __magic_name__								=			trainer.evaluate()
    trainer.log_metrics("""eval""",	A_   )
    trainer.save_metrics("""eval""",	A_   )

  # Write model card and (optionally) push to hub
  __magic_name__								=			{
      """finetuned_from""": model_args.model_name_or_path,
      """tasks""": """image-classification""",
      """dataset""": data_args.dataset_name,
      """tags""": ["""image-classification""", """vision"""],
  }
  if training_args.push_to_hub:
    trainer.push_to_hub(**A_   )
  else:
    trainer.create_model_card(**A_   )


if __name__ == "__main__":
    main()



import math

def a__       (			A_   ):






  '''simple docstring'''





  if 1 < number < 4:
    # 2 and 3 are primes
    return True
  elif number < 2 or number % 2 == 0 or number % 3 == 0:
    # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
    return False

  # All primes number are in format of 6k +/- 1
  for i in range(5,	int(math.sqrt(A_   ) + 1   ),	6   ):
    if number % i == 0 or number % (i + 2) == 0:
      return False
  return True

def a__       (			A_ = 0.1   ):






  '''simple docstring'''





  __magic_name__								=			3
  __magic_name__								=			3

  while primes / (2 * j - 1) >= ratio:
    for i in range(j * j + j + 1,	(j + 2) * (j + 2),	j + 1   ):
      primes += is_prime(A_   )
    j += 2
  return j


if __name__ == "__main__":
    import doctest

    doctest.testmod()

